Business Intelligence Developer Tableau Return To – Introduction The greatest value of a picture is when it forces us to notice what we never expected to see. – John W. Here
Suppose you have some data with you and you want to gather some insights from it. Coding is not your forte and you don’t know how to start.
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Let me tell you this – you can make something as descriptive / insightful as the image below, with gestures as simple as drag and drop. And it doesn’t require an ounce of coding. Now that’s the power of Tableau for you!
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For all those reading this who are familiar with Tableau, can make some basic charts on it and want to know more about its wide horizons, this article is for you.
As for those who have yet to be introduced to the beauty and simplicity of Tableau, take a quick run through Tableau for Beginners. Practice doing some simple visualizations and then rush back here!
In this article, we’ll discuss a few basic Tableau functionalities that help make truly dynamic charts. So let’s get started quickly!
It is not practical to store all the data in one table. In order to avoid anomalies related to updates, the data is almost always distributed in multiple tables that have some relation to each other. Let us understand the same with an example.
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Consider the situation when the Supermarket, on the verge of expansion, realizes that the number of returned orders is increasing day by day. To determine the analysis and come up with the right action plan, they drew the following table to understand the returned products:
As you can see, the bindings have the maximum number of items returned. But judging by the color of the bars, Machines and Tables have the highest percentage of return (returned/purchased):
This looks pretty similar to drawing just another table, but the trick here was that it was created using the combined data from two tables: Orders and Returns. This is known as Joining.
The data is composed of 3 tables: Orders, People and Returns, and the ones we are interested in right now are Orders and Returns.
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To join two tables, at least one common field must be present. Here, Tableau has automatically Inner joined the two tables, based on the commonality of the Irder ID column. By means of an inner join, the combined data consists of only those rows that have the same order ID in both tables.
You can change the join type as well as the join field in a Table, but you need to ensure that it is reasonable.
See how I tried to join the two tables based on Order Row ID and Return Order ID? Since the two are not compatible, we don’t see any entries, and a red plus sign near the circles indicates an error.
Here we have used Inner Join, but you can always choose between Inner, Right, Left and Full Outer based on your requirements.
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Mixing data is quite similar to Joins, with the difference that Joining requires the data to be from the same data source. In the above example, we used different tables from the same Excel file. But combining data occurs when you work with different data sources. Let us understand the same with an example.
Superstore has another vertical in the form of Coffeechain which is spread across as many countries as Superstore. But they are considering closing some of the branches after observing the following plot:
As you can see, there are some branches that are doing just as well as Superstore like California and New York, while many are not, like Iowa and New Mexico. Just like in Joins, the trick here is that both datasets, dealing with both verticals, were stored in different data sources, an Excel file and a TDE database.
Why not imagine the same to gain a better understanding? We will start by merging the Superstore data and the Sample – CoffeeChain database. You can also find the data for the latter here:
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Now that your data is ready, mixed or merged, let’s start making interesting dashboards. From here on, we will only use Superstore data: Orders + Returns (left-joined).
Let’s start by looking at the Survey analysis example. In a food consumption survey, in the Food Preferences section, instead of “Low Fat,” you might have “LF,” or instead of “Regular,” you might have “reg.”
As you can see, due to the different nomenclatures, this visualization is not ideal. So one possible solution to this is grouping where you can put “LF” and “Low Fat” in one group and “reg” and “Regular” in another:
The above is a Return Analysis across categories and their subcategories. Although you can’t see it in the bar graph, copiers have the highest return percentage, followed by furniture:
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As for the Line Chart, it looks like the sales team was right all along. Returns were actually increasing quite quickly, but luckily from what we can see, the increase is slowly receding.
From the pie charts, you can clearly analyze the returns of each category. As can be seen, Technology has suffered maximum number of returns.
Another analysis, which I will leave to you to do, could be the distribution of Returns across different states. Once you’re done learning how the above charts are made, you can easily make this one too. So let’s get started:
This step automatically separates the sales of each subcategory based on whether or not the orders have a value of Null or Yes under Returns.
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Also, in Legends, you’ll likely see “In/Out” as aliases. You can change this to your requirements by right-clicking the blue Input/Output pill in the Tags panel and selecting Edit Alias.
Let’s switch to the pie chart we made. We will apply the same ReturnedOrNot group for this as well. First create two duplicates of this sheet and work on one of them:
All that’s left now is to combine the above worksheets into one dashboard. Why don’t you try making the graph for the feedback state distribution as well?
After observing the return analysis, your organization has decided that the increase in return is not so alarming and should not be considered a reason for not expanding.
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But Superstore will expand only in those states where sales and profits have crossed a certain margin, for example, 40,000 and 10,000 respectively:
So sets, as created above, are really similar to groups. In Set, you group data that meets a specific set condition. Another interpretation could be: Groups help you achieve hierarchy at a higher level, as we saw in the previous example, while sets help you achieve granularity at a lower level.
This step combines the above two conditions for sales and profit to produce the required combined calculation. To view the results:
A line chart is as easy to make as the one we made earlier for Trend Reversion. Here we have excluded the states that belong to the “No Expansion” group, as we have excluded the one that is not returned there.
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Superstore’s database is pretty comprehensive. It offers a lot of information and field kits. But like all data, there is always the opportunity to extract more features. Calculated fields help you do just that, and also allow you to perform simple and complex calculations on data.
To put it in simple words, it is a formula that you apply to your data, where the various measures act as variables.
Just go to Analysis, click on Create Calculated Field and something like this will appear:
This is where you write your formulas, and as you can see, Tableau also provides you with different syntaxes, so you’ll never feel lost! You can also apply “If-Else” conditions, “Case” conditions (as we’ll see next), and of course the usual mathematical calculations, which we’ll explore now.
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So what calculation to start with? Let’s start with something simple i.e. Average sales related to orders. The most appropriate formula for the same would be Total Sales / Total Number of Orders. To convert this formula into Tableau terms, Total Sales implies the SUM of the sales, while Total Orders implies their COUNT.
So what you’ve basically accomplished with the calculated field is you’ve created your own measure, which you can use just like Sales and Profit.
Apparently this was just the gist of what Calculated Fields could do. They can also be used for various complex calculations, and a look at one such example can be seen in the next section.
Just as filters are measures through which you can view different aspects of your data, a parameter is another great feature. It can be used instead of Filters and can display its own dynamic property.
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So what are parameters? These act
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